explicitly handle the structure of the HuggingFace pipeline output

app.py

@@ -37,13 +37,19 @@ def predict_emotion(text):
             # Return default DataFrame directly
             return default_df
         
-        # Get predictions and create DataFrame
+        # Get predictions and handle result structure
         logger.info("Running prediction...")
-        predictions = classifier(text)[0]
-        df = pd.DataFrame(predictions)
-        logger.info(f"Processed scores:\n{df}")
+        predictions = classifier(text)
         
-        return df  # Simply return the DataFrame
+        # Ensure predictions are in appropriate list-of-dicts format
+        if isinstance(predictions, list) and len(predictions) > 0 and isinstance(predictions[0], dict):
+            df = pd.DataFrame(predictions[0])  # Convert list of dicts to DataFrame
+        else:
+            logger.error(f"Unexpected predictions format: {predictions}")
+            return error_df
+
+        logger.info(f"Processed scores:\n{df}")
+        return df
         
     except Exception as e:
         logger.error(f"Error in prediction: {str(e)}")